2 citations · 6 across the 13 of their papers we have counts for
15 papers · 1 filter
NAVER LABS Europe Submission to the Instruction-following 2026 Short Track
Marcely Zanon Boito, Hemant Yadav, Jean-Luc Meunier +1
In this paper, we describe NAVER LABS Europe's submission to the instruction-following speech processing short track at IWSLT 2026. We participate again in the constrained setting,…
StarDrinks: An English and Korean Test Set for SLU Evaluation in a Drink Ordering Scenario
Marcely Zanon Boito, Caroline Brun, Inyoung Kim +5
LLMs and speech assistants are increasingly used for task-oriented interactions, yet their evaluation often relies on controlled scenarios that fail to capture the variability and…
SpeechMapper: Speech-to-text Embedding Projector for LLMs
Biswesh Mohapatra, Marcely Zanon Boito, Ioan Calapodescu
Current speech LLMs bridge speech foundation models to LLMs using projection layers, training all of these components on speech instruction data. This strategy is computationally i…
NAVER LABS Europe Submission to the Instruction-following Track
Beomseok Lee, Marcely Zanon Boito, Laurent Besacier +1
In this paper we describe NAVER LABS Europe submission to the instruction-following speech processing short track at IWSLT 2025. We participate in the constrained settings, develop…
Speech Foundation Models and Crowdsourcing for Efficient, High-Quality Data Collection
Beomseok Lee, Marco Gaido, Ioan Calapodescu +2
While crowdsourcing is an established solution for facilitating and scaling the collection of speech data, the involvement of non-experts necessitates protocols to ensure final dat…
Speech-MASSIVE: A Multilingual Speech Dataset for SLU and Beyond
Beomseok Lee, Ioan Calapodescu, Marco Gaido +2
We present Speech-MASSIVE, a multilingual Spoken Language Understanding (SLU) dataset comprising the speech counterpart for a portion of the MASSIVE textual corpus. Speech-MASSIVE…
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks
Varsha Suresh, Salah Aït-Mokhtar, Caroline Brun +1
Self-supervised learning models have revolutionized the field of speech processing. However, the process of fine-tuning these models on downstream tasks requires substantial comput…
mHuBERT-147: A Compact Multilingual HuBERT Model
Marcely Zanon Boito, Vivek Iyer, Nikolaos Lagos +2
We present mHuBERT-147, the first general-purpose massively multilingual HuBERT speech representation model trained on 90K hours of clean, open-license data. To scale up the multi-…
NAVER LABS Europe's Multilingual Speech Translation Systems for the IWSLT 2023 Low-Resource Track
Edward Gow-Smith, Alexandre Berard, Marcely Zanon Boito +1
This paper presents NAVER LABS Europe's systems for Tamasheq-French and Quechua-Spanish speech translation in the IWSLT 2023 Low-Resource track. Our work attempts to maximize trans…
A Textless Metric for Speech-to-Speech Comparison
Laurent Besacier, Swen Ribeiro, Olivier Galibert +1
In this paper, we introduce a new and simple method for comparing speech utterances without relying on text transcripts. Our speech-to-speech comparison metric utilizes state-of-th…
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation
Cheonbok Park, Hantae Kim, Ioan Calapodescu +2
Domain Adaptation (DA) of Neural Machine Translation (NMT) model often relies on a pre-trained general NMT model which is adapted to the new domain on a sample of in-domain paralle…
Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness
Alexandre Bérard, Ioan Calapodescu, Marc Dymetman +3
We share a French-English parallel corpus of Foursquare restaurant reviews (https://europe.naverlabs.com/research/natural-language-processing/machine-translation-of-restaurant-revi…
Naver Labs Europe's Systems for the Document-Level Generation and Translation Task at WNGT 2019
Fahimeh Saleh, Alexandre Bérard, Ioan Calapodescu +1
Recently, neural models led to significant improvements in both machine translation (MT) and natural language generation tasks (NLG). However, generation of long descriptive summar…
Naver Labs Europe's Systems for the WMT19 Machine Translation Robustness Task
Alexandre Bérard, Ioan Calapodescu, Claude Roux
This paper describes the systems that we submitted to the WMT19 Machine Translation robustness task. This task aims to improve MT's robustness to noise found on social media, like…
Moment Matching Training for Neural Machine Translation: A Preliminary Study
Cong Duy Vu Hoang, Ioan Calapodescu, Marc Dymetman
In previous works, neural sequence models have been shown to improve significantly if external prior knowledge can be provided, for instance by allowing the model to access the emb…